應用類神經網路及管制圖手法於失誤預警系統之研發

The application of neural network and quality control chart in developing early warning system

林志聰

指導教授 ： 黃雪玲 ； 王明揚

國立清華大學/工學院/工業工程與工程管理學系/博士(2010年)

https://doi.org/10.6843/NTHU.2010.00641

摘要

近年來，人為失誤被認為是引發意外的主因之一，不論是何種型態的工廠，人為失誤都可能引起許多有形和無形的損失，而人為失誤的預防對核電廠而言更形重要，不論在核電廠的控制或維修，人都扮演了極為重要的角色，在過去的研究調查指出，核電廠所發生的意外中，人為失誤佔了約三到五成的比重，也因此，本研究核心目標將針對控制和維修環境，分別設計與發展合適之預警機制，以期在失誤發生之前，能及時提醒作業人員並避免災害的發生。
首先，在核電廠主控室的部份，本研究將應用品質管制圖的管制法則於核電廠的重要監控參數上，並同時設計兩種不同的預警介面，過程中，所有受試者將會模擬操作正常與異常兩種不同的情境，而這兩種情境包括了降載反應爐和棄載等兩種情況，實驗因變數則包括了工作績效、心智負荷和情境知覺等項目。而在維修的部份，由於核電廠維修大部份都必需線上完成，因此維修人員在時間壓力下也往往有較高的心智負荷，對此，本研究也將應用類神經GMDH網路建立維修人員之心智負荷預測模式，透過此模式的建立，管理者可分配合適的工作量及時間以避免維修人員心智負荷過重而引發意外。另外，本論文也進一步應用GMDH在IC封裝廠內建立視覺疲勞預警系統，藉以了解方法論在其他產業應用之可行性。
本研究結果指出：(1)在預警系統的輔助下受測者可有效減少警報響起的次數並有較高的次要工作績效。(2)預警系統的導入並不僅不會造成情境知覺的降低，還可有效降低運轉員的工作負荷。(3)在維修方面，由GMDH所建立的模式對心智負荷有不錯的預測能力，未來亦可提供給管理者作為安排維修工作負荷之依據。(4)GMDH方法應用IC封裝廠中，最後結果也顯示模式的導入不僅可降低作業員的視覺疲勞還可有效提高產線的良率。

關鍵字

人為失誤 ； 預警系統 ； 核電廠 ； 封裝廠 ； 自組性演算法 ； 心智負荷

並列摘要

Many studies have showed the human errors were the major reason to cause of the accident. In fact, human errors may result in tangible or intangible cost loss and influence of system safety. Especially in nuclear power plant (NPP), serious human failure would damage to operation safety. Thus, no matter what the roles they are, such as operation or maintenance, each of the people in NPPs is played as a vital role. Previous studies indicate that about 30 to 50 percent accidents were result from human failure. Hence, the aim of this dissertation would design two early warning methods in control room and maintenance department. The proposed early warning methods in this study could decrease human error in both control room and maintenance environment.

First of all, this study applied the concepts of the Shewhart control chart to design a pre-alarm system for NPP control room. Two pre-alarm types were designed to compare with the original system, and all participants were requested to monitor each simulated system under both normal and abnormal states. The tasks for the participants included shutting down the reactor, searching for procedures, monitoring system parameters and executing secondary tasks. In each trial, the task performance, mental workload and situation awareness (SA) of the participants were measured. Respecting to maintenance, currently, on-line maintenance for NPPs is performed quite often while the system is in operation. The limited maintenance time very often bring heavy mental workload to their engineers. Therefore, according to the factors affecting the mental workload, this dissertation would construct a predictive mental workload model while maintaining digital systems in NPP. Through predicting mental workload, the manager can organize the human resources for each daily task to sustain the appropriate mental workload as well as improve maintenance performance. Finally, to verify the feasibility of the proposed scheme, this dissertation further applies GMDH into IC packaging plant to develop a suitable pre-alarm system.

The results indicated (1) that participants had lower mental workload, but equal SA, when monitoring the system with either type of pre-alarm designs, (2) that lower alarm frequency and higher secondary task performance were obtained with the pre-alarm design, (3) that the proposed model is expected to provide the supervisor a reference value of engineers’ mental workload and the prediction ability of the model was high. (4) GMDH can construct a reliable pre-alarm system to reduce operators’ visual fatigue and raise yield rate in IC packaging plant.

並列關鍵字

Human error ； pre-alarm system ； nuclear power plant (NPP) ； integrated circuit (IC) packaging plant ； group method of data handling (GMDH) ； mental workload

參考文獻

Abdel-Aal, R. E. (2008). Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks. Computers & Industrial Engineering, 54(4), 903-917.

Artman, H. (2000). Team situation assessment and information distribution. Ergonomics, 43(8), 1111-1128.

Baron, M., Takken, A., Yashchin, E., & Lanzerotti, M. (2008). Modeling and Forecasting of Defect-Limited Yield in Semiconductor Manufacturing. Ieee Transactions on Semiconductor Manufacturing, 21(4), 614-624.

Bergqvist, U., Knave, B., Voss, M., & Wibom, R. (1992). A longitudinal study of VDT work and health. International Journal of Human-Computer Interaction, 4, 197-219.

Bergqvist, U. O., & Knave, B. G. (1994). Eye Discomfort and Work with Visual-Display Terminals. Scandinavian Journal of Work Environment & Health, 20(1), 27-33.